The rapid development of crowdsourcing has enriched workers’ experience and skills of workers， making them more aware of tasks and tend to complete multiple tasks at the same time. Therefore， assigning tasks according to workers’ subjective preferences has become a common way of task assignment. However， out of personal interests， workers may take malicious bidding behaviors to obtain higher utility. It is detrimental to the development of crowdsourcing platforms. To this end， an incentive mechanism of crowdsourcing multi-task assignment against malicious bidding was proposed， named GIMSM （Greedy Incentive Mechanism for Single-Minded）. First， a linear ratio was defined as the allocation basis by this mechanism. Then， according to the greedy strategy， from a sequence of increasing worker ratios， tasks were selected and assigned. Finally， the workers selected by allocation algorithm were paid according to payment function， and the result of task assignment was obtained. The experiments were conducted on Taxi and Limousine Commission Trip Record Data dataset. Compared to TODA （Truthful Online Double Auction mechanism）， TCAM （Truthful Combinatorial Auction Mechanism） and FU method， GIMSM’s average quality level of task results under different numbers of workers increased by 25.20 percentage points， 13.20 percentage points and 4.40 percentage points， respectively. GIMSM’s average quality level of task results under different numbers of tasks increased by 26.17 percentage points， 16.17 percentage points and 9.67 percentage points， respectively. In addition， the proposed mechanism GIMSM satisfies individual rationality and incentive compatibility， and can obtain task assignment results in linear time. The experimental results show that the proposed mechanism GIMSM has good anti-malicious bidding performance， and has a better performance on the crowdsourcing platforms with a large amount of data.

The existing pedestrian multi-object tracking algorithms have the problems of undetectable pedestrians and inter-frame association confusion in dense scenes. In order to improve the precision of pedestrian tracking in dense scenes， a head tracking model based on full-body appearance features was proposed， namely HT-FF （Head Tracking with Full-body Features）. Firstly， the head detector was used to replace the full-body detector to improve the detection rate of pedestrians in dense scenes. Secondly， using the information of human posture estimation as a guide， the noise-removed full-body appearance features were obtained as tracking clues， which greatly reduced the confusion in the association among multiple frames. HT-FF model achieves the best results on multiple indicators such as MOTA （Multiple Object Tracking Accuracy） and IDF1 （ID F1 Score） on benchmark dataset of pedestrian tracking in dense scenes — Head Tracking 21 （HT21）. The HT-FF model can effectively alleviate the problem of lost and confused pedestrian tracking in dense scenes， and the proposed tracking model combining multiple clues is a new paradigm of pedestrian tracking model.

Recently， the leading human pose estimation algorithms are heatmap-based algorithms. Heatmap decoding （i.e. transforming heatmaps to coordinates of human joint points） is a basic step of these algorithms. The existing heatmap decoding algorithms neglect the effect of systematic errors. Therefore， an error compensation based heatmap decoding algorithm was proposed. Firstly， an error compensation factor of the system was estimated during training. Then， the error compensation factor was used to compensate the prediction errors including both systematic error and random error of human joint points in the inference stage. Extensive experiments were carried out on different network architectures， input resolutions， evaluation metrics and datasets. The results show that compared with the existing optimal algorithm， the proposed algorithm achieves significant accuracy gain. Specifically， by using the proposed algorithm， the Average Precision （AP） of the HRNet-W48-256×192 model is improved by 2.86 percentage points on Common Objects in COntext （COCO）dataset， and the Percentage of Correct Keypoints with respect to head （PCKh） of the ResNet-152-256×256 model is improved by 7.8 percentage points on Max Planck Institute for Informatics （MPII）dataset. Besides， unlike the existing algorithms， the proposed algorithm did not need Gaussian smoothing preprocessing and derivation operation， so that it is 2 times faster than the existing optimal algorithm. It can be seen that the proposed algorithm has applicable values to performing fast and accurate human pose estimation.